(2019) Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis : a machine learning approach. The Lancet Digital Health, 1 (6). E261-E270. doi:10.1016/S2589-7500(19)30121-9 ISSN 2589-7500.

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Abstract

Background
Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multivariable prediction models of 1-year remission and recovery outcomes using baseline clinical variables in people with first-episode psychosis.

Methods
In this machine learning approach, we applied supervised machine learning, using regularised regression and nested leave-one-site-out cross-validation, to baseline clinical data from the English Evaluating the Development and Impact of Early Intervention Services (EDEN) study (n=1027), to develop and internally validate prediction models at 1-year follow-up. We assessed four binary outcomes that were recorded at 1 year: symptom remission, social recovery, vocational recovery, and quality of life (QoL). We externally validated the prediction models by selecting from the top predictor variables identified in the internal validation models the variables shared with the external validation datasets comprised of two Scottish longitudinal cohort studies (n=162) and the OPUS trial, a randomised controlled trial of specialised assertive intervention versus standard treatment (n=578).

Findings
The performance of prediction models was robust for the four 1-year outcomes of symptom remission (area under the receiver operating characteristic curve [AUC] 0·703, 95% CI 0·664–0·742), social recovery (0·731, 0·697–0·765), vocational recovery (0·736, 0·702–0·771), and QoL (0·704, 0·667–0·742; p<0·0001 for all outcomes), on internal validation. We externally validated the outcomes of symptom remission (AUC 0·680, 95% CI 0·587–0·773), vocational recovery (0·867, 0·805–0·930), and QoL (0·679, 0·522–0·836) in the Scottish datasets, and symptom remission (0·616, 0·553–0·679), social recovery (0·573, 0·504–0·643), vocational recovery (0·660, 0·610–0·710), and QoL (0·556, 0·481–0·631) in the OPUS dataset.

Interpretation
In our machine learning analysis, we showed that prediction models can reliably and prospectively identify poor remission and recovery outcomes at 1 year for patients with first-episode psychosis using baseline clinical variables at first clinical contact.

Item Type:	Journal Article
Divisions:	Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Mental Health and Wellbeing Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Journal or Publication Title:	The Lancet Digital Health
Publisher:	Elsevier Inc.
ISSN:	2589-7500
Official Date:	October 2019
Dates:	Date Event October 2019 Published 12 September 2019 Available 12 September 2019 Accepted
Volume:	1
Number:	6
Page Range:	E261-E270
DOI:	10.1016/S2589-7500(19)30121-9
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	17 September 2019
Date of first compliant Open Access:	17 September 2019

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